[R] mboost: Interpreting coefficients from glmboost if center=TRUE

[Kyle Werner]

Thanks for your reply. In fact, I do use the predict method for model
assessment, and it shows that centering leads to a substantial
improvement using even the bluntest of assessments of 'goodness'
(i.e., binary categorization accuracy). So I agree that the package
authors must have internal tools to reverse the effects of centering
the variables, at least within the predict method. But it seems to me
that the coefficients that I get out should be related to the values
that I input, not to the centered values. In other words, centering
seems like it should be done "invisibly;" unless I center the
variables myself, I would expect the coefficients to be applicable to
the original data.

I extract the coefficients returned by the model and store them in a
database which is web accessible. I reconstruct models periodically,
and track various statistics associated with these models in the
database. This is why I highly value the fact that mboost has
glmboost, which can return linearly interpretable coefficients. It is
also why I do not directly call upon R every time I want to query a
model. (As an aside, if I were to use R directly, I might consider the
gamboost or blackboost methods, which do not return scalar
coefficients that are readily extractable.)



On Sun, Feb 7, 2010 at 6:31 PM, David Winsemius <dwinsemius at comcast.net> wrote:
>
> On Feb 7, 2010, at 5:03 PM, Kyle Werner wrote:
>
>> I'm running R 2.10.1 with mboost 2.0 in order to build predictive
>> models . I am performing prediction on a binomial outcome, using a
>> linear function (glmboost). However, I am running into some confusion
>> regarding centering. (I am not aware of an mboost-specific mailing
>> list, so if the main R list is not the right place for this topic,
>> please let me know.)
>>
>> The boost_control() function allows for the choice between center=TRUE
>> and center=FALSE. If I select center=FALSE, I am able to interpret the
>> coefficients just like those from standard logistic regression.
>> However, if I select center=TRUE, this is no longer the case. In
>> theory and in practice with my data, centering improves the
>> predictions made by the model, so this is an issue worth pursuing for
>> me.
>>
>> Below is output from running the exact same data in exactly the same
>> way, only differing by whether the "center" bit is flipped or not:
>>
>> Output with center=TRUE:
>> [(Intercept)] => -0.04543632
>> [painscore] => 0.007553608
>> [Offset] => -0.546520621809327
>>
>> Output with center=FALSE:
>> [(Intercept)] => -0.989742
>> [painscore] => 0.001342585
>> [Offset] => -0.546520621809327
>>
>> The mean of painscore is 741. It seems to me that for center=FALSE,
>> mboost should modify the intercept by subtracting 741*0.007553608 from
>> it (thus intercept should = -11.285). If I manually do this, the
>> output is credible, and in the ballpark of that given by other methods
>> (e.g., lrm or glm with a Binomial link function). If I don't do this,
>> then the inverse logistic interpretation of the output is off by
>> orders of magnitude.
>>
>> In the end, with "center=TRUE", and I want to make a prediction based
>> on the coefficients returned by mboost, the results only make sense if
>> I manually rescale my independent variables prior to making a
>> prediction. Is this the desired behavior, or am I doing something
>> wrong?
>
> I don't know, but my question is ... why aren't you using the predict method
> for that sort of object? Presumably the authors of the package know how to
> recognize the differences in the objects. Testing confirms this to be the
> case with the first example in the glmboost help page.
>
>
>>
>> Many thanks.
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>
> David Winsemius, MD
> Heritage Laboratories
> West Hartford, CT
>
>